diff --git a/python/paddle/fluid/tests/unittests/test_conv_transpose_nn_grad.py b/python/paddle/fluid/tests/unittests/test_conv_transpose_nn_grad.py
index a4ef15b1f0db3c3737c063d4471cc54ef24ea074..b9e9224b9e4029d0d9a70b75a1c30c5a8278a6e6 100644
--- a/python/paddle/fluid/tests/unittests/test_conv_transpose_nn_grad.py
+++ b/python/paddle/fluid/tests/unittests/test_conv_transpose_nn_grad.py
@@ -27,6 +27,9 @@ from decorator_helper import prog_scope
 
 
 class TestConvTransposeDoubleGradCheck(unittest.TestCase):
+    def conv_transpose_wrapper(self, x):
+        return paddle.nn.functional.conv2d_transpose(x[0], x[1], groups=1)
+
     @prog_scope()
     def func(self, place):
         shape = [2, 4, 3, 3]
@@ -55,6 +58,11 @@ class TestConvTransposeDoubleGradCheck(unittest.TestCase):
         else:
             gradient_checker.double_grad_check(
                 [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.conv_transpose_wrapper, [x] + w,
+            y,
+            x_init=[x_arr] + w_arr,
+            place=place)
 
     def test_grad(self):
         places = []
@@ -67,6 +75,10 @@ class TestConvTransposeDoubleGradCheck(unittest.TestCase):
 
 class TestConvTranspose2DoubleGradCheck_AsyPadding(
         TestConvTransposeDoubleGradCheck):
+    def conv_transpose_wrapper(self, x):
+        return paddle.nn.functional.conv2d_transpose(
+            x[0], x[1], groups=1, padding=[1, 0, 0, 1])
+
     @prog_scope()
     def func(self, place):
         shape = [2, 2, 3, 3]
@@ -100,10 +112,19 @@ class TestConvTranspose2DoubleGradCheck_AsyPadding(
         else:
             gradient_checker.double_grad_check(
                 [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.conv_transpose_wrapper, [x] + w,
+            y,
+            x_init=[x_arr] + w_arr,
+            place=place)
 
 
 class TestConvTranspose2DoubleGradCheck_PaddingSAME(
         TestConvTransposeDoubleGradCheck):
+    def conv_transpose_wrapper(self, x):
+        return paddle.nn.functional.conv2d_transpose(
+            x[0], x[1], groups=1, padding="SAME")
+
     @prog_scope()
     def func(self, place):
         shape = [2, 2, 3, 3]
@@ -137,10 +158,19 @@ class TestConvTranspose2DoubleGradCheck_PaddingSAME(
         else:
             gradient_checker.double_grad_check(
                 [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.conv_transpose_wrapper, [x] + w,
+            y,
+            x_init=[x_arr] + w_arr,
+            place=place)
 
 
 class TestConvTranspose2DoubleGradCheck_PaddingVALID(
         TestConvTransposeDoubleGradCheck):
+    def conv_transpose_wrapper(self, x):
+        return paddle.nn.functional.conv2d_transpose(
+            x[0], x[1], groups=1, padding="VALID")
+
     @prog_scope()
     def func(self, place):
         shape = [2, 2, 3, 3]
@@ -174,10 +204,19 @@ class TestConvTranspose2DoubleGradCheck_PaddingVALID(
         else:
             gradient_checker.double_grad_check(
                 [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.conv_transpose_wrapper, [x] + w,
+            y,
+            x_init=[x_arr] + w_arr,
+            place=place)
 
 
 class TestConvTranspose2DoubleGradCheck_ChannelLast(
         TestConvTransposeDoubleGradCheck):
+    def conv_transpose_wrapper(self, x):
+        return paddle.nn.functional.conv2d_transpose(
+            x[0], x[1], groups=1, padding=[1, 1], data_format="NHWC")
+
     @prog_scope()
     def func(self, place):
         shape = [2, 3, 3, 2]
@@ -213,6 +252,11 @@ class TestConvTranspose2DoubleGradCheck_ChannelLast(
         else:
             gradient_checker.double_grad_check(
                 [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.conv_transpose_wrapper, [x] + w,
+            y,
+            x_init=[x_arr] + w_arr,
+            place=place)
 
 
 if __name__ == "__main__":
diff --git a/python/paddle/utils/code_gen/backward.yaml b/python/paddle/utils/code_gen/backward.yaml
index 3de9e323c2ed98365c2e8085cdba1a1f52db28dc..ff49fd426146b261bc5dc5e692cbcf14b9f99756 100644
--- a/python/paddle/utils/code_gen/backward.yaml
+++ b/python/paddle/utils/code_gen/backward.yaml
@@ -301,6 +301,16 @@
     use_gpudnn : true
   optional : grad_input_grad, grad_filter_grad
 
+- backward_api : conv2d_transpose_double_grad
+  forward : conv2d_transpose_grad(Tensor x, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(grad_x), Tensor(grad_filter)
+  args : (Tensor x, Tensor filter, Tensor grad_out, Tensor grad_x_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
+  output : Tensor(x_grad), Tensor(filter_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : Conv2dTransposeDoubleGradInferMeta
+  kernel :
+    func : conv2d_transpose_grad_grad
+    use_gpudnn : true
+
 - backward_api : conv2d_transpose_grad
   forward : conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
   args : (Tensor x, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
@@ -310,6 +320,7 @@
   kernel :
     func : conv2d_transpose_grad
     use_gpudnn : true
+  backward : conv2d_transpose_double_grad
 
 - backward_api : conv3d_transpose_grad
   forward : conv3d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)